D465 Data Applications
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Free D465 Data Applications Questions
An analyst working in Google Sheets wants to clean, organize, sort, group, summarize large quantities, and use calculations to find insights and trends in data. Which function should be used?
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Conditional formatting
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Pivot table
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Fill handle
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Remove duplicates
Explanation
Explanation
Correct answer: (B.) Pivot table
A pivot table is designed for high-level data analysis, allowing an analyst to group, sort, summarize, and perform calculations on large datasets efficiently. It enables dynamic aggregation of data, such as totals, averages, and counts, while also supporting filtering and restructuring to reveal patterns and trends. Conditional formatting only changes visual appearance based on rules, the fill handle is used for auto-filling patterns or formulas, and remove duplicates is only for eliminating repeated entries, so none of these provide the comprehensive analytical capabilities of a pivot table.
What is a benefit of filtering out any outliers from a data set?
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Data is permanently changed.
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Data will be sorted for further analysis.
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Data will be duplicated for another file.
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Data can be returned to its original organization.
Explanation
Explanation
Correct answer: (D.) Data can be returned to its original organization.
Filtering data temporarily hides certain values—such as outliers—without permanently altering the original dataset. This allows analysts to focus on typical data patterns while retaining the ability to restore the full dataset at any time. Unlike deleting or modifying data, filtering is reversible, making it a safe and flexible method for analysis. Therefore, one key benefit is that the data can easily be returned to its original state.
A data analyst runs code for data visualization using ggplot2.
ggplot(data = fish + geom_point(mapping = aes(x = fin_length_mm, y = body_mass_g)).
What is the problem with the code?
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A plus symbol is used at the end of the line.
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The closing parenthesis is missing.
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The first letter 'g' in ggplot should be capitalized to be Ggplot.
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The opening parenthesis is missing.
Explanation
Explanation
Correct answer: (B.) The closing parenthesis is missing.
The code attempts to create a scatter plot in ggplot2 by starting with the ggplot() function and then adding a geom_point layer using the + operator. In proper ggplot2 syntax, every ggplot call must open with a parenthesis after the function name and must have a matching closing parenthesis at the very end of the complete layered expression. Here, the code opens correctly with ggplot( but the final closing parenthesis is absent after the geom_point layer, leaving the function call incomplete and causing a syntax error in R. The + symbol is correctly placed between layers, the function name "ggplot" must remain lowercase, and an opening parenthesis is present. The only actual problem is the missing closing parenthesis that terminates the entire ggplot expression.
Which of the following is a key application of data science in healthcare that involves using historical data to forecast future health outcomes?
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Predictive analytics
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Medical imaging
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Personalized medicine
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Data visualization
Explanation
Explanation:
Predictive analytics in healthcare leverages historical patient data, such as medical histories, lab results, and demographic information, to forecast future health outcomes. This approach helps healthcare providers anticipate disease risks, prevent complications, and make informed treatment decisions. Unlike medical imaging, personalized medicine, or data visualization, predictive analytics specifically focuses on using past data to predict future events.
Correct Answer:
Predictive analytics
Why does an analyst use the JOIN command in Structured Query Language?
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To update data in a database
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To select a certain number of records
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To combine rows and provide an output from two or more tables
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To delete data from a database
Explanation
Explanation
Correct answer: (C.) To combine rows and provide an output from two or more tables
The JOIN clause in SQL is used to combine rows from two or more tables based on a related column between them. This allows analysts to retrieve and work with connected datasets in a single query output, enabling richer analysis across relational data structures. It does not modify data (UPDATE or DELETE) nor limit the number of records returned. Instead, its core purpose is integrating data from multiple tables into one result set.
Which package in the Tidyverse is the analyst using?
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Tidyr
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Ggplot2
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Readr
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Dplyr
Explanation
Explanation
Correct answer: (D.) Dplyr
The Tidyverse is a collection of packages in R, and the correct package depends on the task being performed. However, when the question does not specify a single operation and is framed generally around data manipulation within a structured workflow, the most commonly used core package is dplyr, which is designed for data wrangling tasks such as filtering, selecting, mutating, and summarizing data. Tidyr is focused on reshaping data, ggplot2 is used for visualization, and readr is used for importing data from files.
An analyst wants to convert an R Markdown file to an output that publishes analysis slides for stakeholders. Which output format should the analyst create?
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GitHub document
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Presentation
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Dashboard
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Notebook
Explanation
Explanation
Correct answer: (B.) Presentation
In R Markdown, a presentation output format is used to generate slide-based reports intended for stakeholder communication. It converts analysis results into a sequence of slides that can include text, code output, and visualizations, making it suitable for formal presentations. Dashboards are used for interactive visual summaries, notebooks combine code and narrative for reproducible analysis, and GitHub documents are not a standard R Markdown output type.
Which function in R can be used to control for bias by injecting a randomization element to data?
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sd()
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bias()
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ggplot2()
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sample()
Explanation
Explanation
Correct answer: (D.) sample()
In R, the sample() function is used to randomly select elements from a dataset or to shuffle data, introducing randomization into the analysis process. This randomness is important for reducing selection bias, especially in tasks like creating training and test datasets or performing random sampling. The other options are not used for this purpose: sd() calculates standard deviation, ggplot2() is a visualization package, and bias() is not a standard R function.
An analyst is working with a R Markdown file and needs to add the name of the Tidyverse package to the code. What does the analyst add before and after “tidyverse” and then again after the data set to do this?
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Hashtags
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Apostrophes
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Asterisks
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Calculated field
Explanation
Explanation
Correct answer: (B.) Apostrophes
In R Markdown and R code contexts, names such as packages or strings are often enclosed in quotes to indicate they are character values. The Tidyverse package name would typically be written as "tidyverse" when treated as a string. Apostrophes or quotation marks are used to define text literals so that the interpreter recognizes them correctly. Hashtags are used for comments, asterisks are used for text formatting in Markdown, and calculated fields are used in data analysis contexts rather than code syntax.
What is the primary purpose of the Skimr package in R?
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It helps to clean the data.
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It helps to manipulate the data.
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It helps to visualize the data.
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It helps to summarize the data.
Explanation
Explanation
Correct answer: (D.) It helps to summarize the data.
The Skimr package is used to quickly generate summary statistics and an overview of datasets in R. It provides a structured, readable summary of variables, including missing values, distributions, and key descriptive statistics, which helps analysts understand the overall shape and quality of the data. It is not designed for data cleaning, manipulation, or visualization, but rather for efficient data summarization at the exploratory analysis stage.
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